In a related work, [1]} proposed two deep neural networks architectures, where they reused the basic elements of GoogLeNet and VGG net for face recognition, the resulting network DeepID3 was rebuilt from inception layers and stacked convolution proposed in GoogLeNet and VGG net to make it appropriate for face recognition. Supervisory signs for identity verification were added to each of the intermediate and final feature extraction layers throughout the training. Their method achieved \(99.53\%\)  facial verification accuracy on the faces scanned in life wild database (LFW).
